Large machinery, such as power generation equipment, is typically very expensive to purchase, install, maintain and operate. Accordingly, determining whether such equipment is operating within desired operating parameters is important. Detecting conditions that indicate that the equipment is operating outside these desired parameters, which may result in damage to the equipment is, therefore, also important. In order to detect such conditions, sensors are typically used to measure operating parameters, such as pressure, temperature, etc., of various components and, if a predetermined threshold for a particular parameter is crossed by a particular measurement, a fault is declared. Recently, learning techniques for fault detection systems have become more prevalent in attempts to improve the accuracy of determining whether a fault exists. Well-known techniques, such as neural networks, multivariate state estimation techniques (MSET) and fuzzy logic have been used for such purposes. All such methods use historical data, indicative of past normal operations and fault conditions, to monitor future data generated by operations of the equipment. If the future data deviates too much from the historical data model, an alarm is generated and a fault is declared.
While prior fault detection methods were advantageous in many implementations, they were also disadvantageous in certain regards. Specifically, these prior fault detection methods typically relied on historical data to generate estimates of the boundaries between data measurements that could be considered faults and those measurements that could be considered normal operating conditions. However, these boundary estimates were typically relatively inaccurate. Therefore, due to this inaccuracy, these methods could potentially identify system faults as normal operating conditions. Similarly, a normal operating condition could be classified as a fault simply because it was not previously observed in the historical data. Such normal, not previously observed conditions are referred to herein as out-of-range conditions.